A deep learning approach to increase the value of satellite data for PM2.5 monitoring in China
- 1School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
- 2Department of Precision Machinery and Precision Instrumentation, University of Scienceand8 Technology of China, Hefei 230027, China
- 3Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
- 4Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- 5Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230027, China
- 6Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
- 7Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA52242, USA
- 8Department of Geography, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR, China
- 9John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
- 1School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
- 2Department of Precision Machinery and Precision Instrumentation, University of Scienceand8 Technology of China, Hefei 230027, China
- 3Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
- 4Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- 5Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230027, China
- 6Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
- 7Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA52242, USA
- 8Department of Geography, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR, China
- 9John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Abstract. Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, such attempts are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be constrained with satellite remote sensing under cloudy/hazy conditions or during nighttime. We introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We use sensitivity analysis and visualization technology to open the neural network black box data model, and quantitatively discuss the potential impact of the input data on the target variables. This technique provides ground-level PM2.5 concentrations with high spatial resolution (0.01°) and 24-hour temporal coverage. Better constrained spatiotemporal distributions of PM2.5 concentrations will help improve health effects studies, atmospheric emission estimates, and predictions of air quality.
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Bo Li et al.
Status: open (until 28 Aug 2022)
Bo Li et al.
Data sets
POI the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=330
GDP the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=252
Population the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=251
MODIS land cover type Mark Friedl https://doi.org/10.5067/MODIS/MCD12C1.006
DEM the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=123
MODIS aerosol optical depth Rob Levy and Christina Hsu https://doi.org/10.5067/MODIS/MOD04_3K.061
Himawari-8 satellite aerosol optical depth Yoshida, M. https://doi.org/10.2151/jmsj.2018-039
site pm2.5 CNEMC http://www.cnemc.cn/
weather fields the National Centers for Environment Prediction https://www.mmm.ucar.edu/weather-research-and-forecasting-model
road network openstreetmap https://download.geofabrik.de/asia/china.html
Bo Li et al.
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